# cell type annotations
display_df(cell_df_complete)

# organ annotations
display_df(organ_df_complete)

# stage annotations
display_df(stage_df_complete)

experiment_id = "SRP487479"

path_to_create_exp_script = "/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py" 
path_to_barcodes_script = "/Users/scarsana/Desktop/git/scRNA-Seq/scripts/create_scRNASeq_barcode_v2.py"
experiment_type = "scRNA"

path_to_output_main = "/Users/scarsana/Desktop/git/expression-annotations/Notebooks/scRNA/" 
path_to_output = "{}{}/".format(path_to_output_main, experiment_id)
library_path_from_script = "{}scRNASeqLibrary_{}.tsv".format(path_to_output, experiment_id)
experiment_path_from_script = "{}scRNASeqExperiment_{}.tsv".format(path_to_output, experiment_id)
barcode_path = "{}scRNASeq_barcode_{}.tsv".format(path_to_output, experiment_id)
barcode_to_add_path = "{}complete_scRNASeq_barcode_{}.tsv".format(path_to_output, experiment_id)
library_to_add_path = "{}complete_scRNASeqLibrary_{}.tsv".format(path_to_output, experiment_id)
experiment_to_add_path = "{}complete_RNASeqExperiment_{}.tsv".format(path_to_output, experiment_id)
script_file = "{}.ipynb".format(experiment_id)
commit_message_exp = '"adding annotated scRNA experiment {}"'.format(experiment_id)
commit_message_py = '"adding annotation files for {} to notebook folder"'.format(experiment_id)



## to add to git
path_to_git_annotations = "/Users/scarsana/Desktop/git/expression-annotations/scRNA_Seq/"
git_library_path = "{}scRNASeqLibrary_merged.tsv".format(path_to_git_annotations)
git_experiment_path = "{}scRNASeqExperiment.tsv".format(path_to_git_annotations)
git_barcode_path = "{}scRNASeq_barcode_{}.tsv".format(path_to_git_annotations, experiment_id)

## columns
library_cols = ['#libraryId', 'experimentId', 'platform', 'SRSId', 'anatId', 'anatName', 'cellTypeId', 'cellTypeName', 'stageId', 'stageName', 'url_GSM', 'infoOrgan', 'infoCellType_abInitio', 'infoCellType_inferred', 'clusterId', 'clusterName', 'infoStage', 'anatAnnotationStatus', 'cellTypeAnnotationStatus', 'stageAnnotationStatus', 'sex', 'strain', 'genotype', 'speciesId', 'RNAseqTags', 'protocol', 'protocolType', 'lib_name', 'sampleName', 'comment', 'condition', 'annotatorId', 'lastModificationDate']
barcode_cols = ['barcode', 'cluster', 'library', 'experiment', 'tissue', 'cell_type', 'anatId_a_posteriori', 'anatName_a_posteriori', 'anat_a_posteriori_annotationStatus', 'cellTypeId', 'cellTypeName', 'cellTypeAnnotationStatus', 'name_Library', 'comments']

## annotation sheet paths (maybe make these into their own folder)
annotation_files_path = "{}annotation_files/".format(path_to_output)
import os
if not os.path.exists(annotation_files_path):
    os.makedirs(annotation_files_path)

info_anat_path = "{}info_anat.tsv".format(annotation_files_path)
info_anat_complete_path = "{}info_anat_complete.tsv".format(annotation_files_path)
info_stage_path = "{}info_dev_stage.tsv".format(annotation_files_path)
info_stage_complete_path = "{}info_dev_stage_complete.tsv".format(annotation_files_path)
info_celltype_path = "{}info_celltype.tsv".format(annotation_files_path)
info_celltype_complete_path = "{}info_celltype_complete.tsv".format(annotation_files_path)

## barcode file & matching library names - 10X only
path_to_barcode_file = "{}barcode_{}.tsv".format(path_to_output, experiment_id)
lib_names_path = "{}matching_lib_names_{}.tsv".format(path_to_output, experiment_id)

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
from IPython.display import display, HTML
import csv
import scanpy as sc

# displays df with the scrollbar next to the DataFrame
def display_df(df):
    pd.set_option("display.max_rows", None)
    pd.set_option("display.max_columns", None)
    display(HTML("<div style='height: 200px; overflow: auto; width: fit-content'>" +
        df.style.to_html(index=False) + "</div>"))

# function that compares two columns in a dataframe and tells you which ones are not equal (case insensitive)
def compare_columns(df, col1, col2, return_col):
    compare_return = df[col1].str.lower() != df[col2].str.lower()  
    df.loc[compare_return, return_col] 
    if not any(compare_return):
        print("The two columns are equal (case insensitive)")
    else:
        print("The following rows are not equal: ")
        print(df.loc[compare_return, [return_col, col1, col2]])

# fixes formatting of file to match libreoffice settings/historic file format
def update_format(path):
    with open(path, 'r') as file:
        filedata = file.read()
    # Replace the target string
    filedata = filedata.replace("\t\"\"", "\t")
    # Write the file out again
    with open(path, 'w') as file:
        file.write(filedata)

# checks for duplicate values in a specific column and prints those values + the corresponding library id
def dup_check(df, column):
    duplicateCheck = df.duplicated(subset=[column], keep=False)
    duplicateCheck.sort_values(inplace=True)
    if duplicateCheck.unique().any() == False:
        print("no duplicate values in " + column)
    elif duplicateCheck.unique().any() == True and column != '#libraryId':
        dups = df[duplicateCheck].loc[:,['#libraryId', column]]
        df_dups = pd.DataFrame(dups)
        df_dups.sort_values(inplace=True, by=column)
        print(df_dups)
    elif duplicateCheck.unique().any() == True and column == '#libraryId':
        print(df[duplicateCheck].loc[:,['#libraryId']])

# prints all unique values in a specific column
def unique_sorted(df, column):
    unique = df[column].unique()
    unique.sort()
    print(unique)

/var/folders/b5/crkp117d43q5mcndnwlrww3w0000gn/T/ipykernel_12612/2872653486.py:3: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd

! python3 $path_to_create_exp_script $experiment_id $path_to_output $experiment_type

/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:97: SyntaxWarning: invalid escape sequence '\('
  all_protoc = [w.replace('(', '\(') for w in all_protoc]
/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:98: SyntaxWarning: invalid escape sequence '\)'
  all_protoc = [w.replace(')', '\)') for w in all_protoc] 
/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:11: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd
Be patient, it may take a few minutes.
0it [00:00, ?it/s]
0 samples dont have attributes, try to find them somewhere else
0it [00:00, ?it/s]
0 samples dont have attributes

library = pd.read_csv(library_path_from_script, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(library)

infoOrgan = library['infoOrgan'].drop_duplicates().sort_values()
organ_df = pd.DataFrame(infoOrgan)
organ_df = organ_df.reindex(columns=[*organ_df.columns.tolist(), 'notes_anat', 'anatId_', 'anatName_', 'anatAnnotationStatus_', 'infoOrgan_new', 'comment_anat'], fill_value="")
organ_df.to_csv(info_anat_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
if not os.path.exists(info_anat_complete_path): 
    organ_df.to_csv(info_anat_complete_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# options for anatAnnotationStatus: perfect match, missing child term, other

organ_df_complete = pd.read_csv(info_anat_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(organ_df_complete)

library = library.merge(organ_df_complete, left_on='infoOrgan', right_on='infoOrgan')

library['anatId'] = library['anatId_'].values
library['anatName'] = library['anatName_'].values
library['anatAnnotationStatus'] = library['anatAnnotationStatus_'].values

## optional - update infoOrgan, add anat-specific comments 
#library['infoOrgan'] = library['infoOrgan_new'].values
#library['comment'] = library[['comment', 'comment_anat']].agg(' '.join, axis=1)

# reformat, save progress
library = library[library_cols]
display_df(library)
library.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

library = pd.read_csv(library_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(library)

infoStage = library['infoStage'].drop_duplicates().sort_values()
stage_df = pd.DataFrame(infoStage)
stage_df = stage_df.reindex(columns=[*stage_df.columns.tolist(), 'notes_stage', 'stageId_', 'stageName_', 'stageAnnotationStatus_', 'infoStage_new','comment_stage'], fill_value="")
stage_df.to_csv(info_stage_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
if not os.path.exists(info_stage_complete_path): 
    stage_df.to_csv(info_stage_complete_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# options for stageAnnotationStatus: perfect match, missing child term, other

stage_df_complete = pd.read_csv(info_stage_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(stage_df_complete)

library = library.merge(stage_df_complete, left_on='infoStage', right_on='infoStage')

library['stageId'] = library['stageId_'].values
library['stageName'] = library['stageName_'].values
library['stageAnnotationStatus'] = library['stageAnnotationStatus_'].values

## optional - update infoOrgan, add anat-specific comments 
#library['infoStage'] = library['infoStage_new'].values
#library['comment'] = library[['comment', 'comment_stage']].agg(' '.join, axis=1)

# reformat, save progress
library = library[library_cols]
display_df(library)
library.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

#library.loc[library["sex"] == "male", "sex"] = "M"
#library.loc[library["sex"] == "female", "sex"] = "F"

#library.loc[:,'strain'] = ''

#library.loc[:,'genotype'] = ''

#library.loc[:,'speciesId'] = ''

# view
display_df(library)

# making these variables because we use them again in the experiment file
# usually 10X Genomics V1/V2/V3/V3.1, Smart-seq, Smart-seq2
my_protocol = '10X Genomics V3.1'
# Full-length or 3'end or 5'end
my_protocolType = "3'end"

library.loc[:,'protocol'] = my_protocol
library.loc[:,'protocolType'] = my_protocolType
# scRNA-seq, Sn-scRNA-seq
library.loc[:,'RNAseqTags'] = 'scRNA-seq'

# view
display_df(library)

# ex. control, diet, light, reproductive capacity, time post mortem, time post feeding, 
# exercise details, menstruation, personality, litter size 
#library.loc[library["condition"] == "old", "condition"] = "new"

# view
display_df(library)

library.loc[:,'annotatorId'] = 'SAC'
library.loc[:,'lastModificationDate'] = '2024-01-30'

# view
display_df(library)

#library.loc[:,'comment'] = ''

library_file_complete = library[library_cols]

library_file_complete.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# view
display_df(library_file_complete)

barcode_column = "barcodes"
library_column = "library"
cell_type_column = "celltype.l2"

! python3 $path_to_barcodes_script $path_to_output --scRNASeqLibrary $library_to_add_path --barcode_file $path_to_barcode_file --barcode_col $barcode_column --libName_col $library_column --cellType_col $cell_type_column --ignore_check --no_additional_file

/Users/scarsana/Desktop/git/scRNA-Seq/scripts/create_scRNASeq_barcode_v2.py:10: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd
There are 4 unique libraries in scRNASeqLibrary.tsv table
There are 4 unique libraries in the barcode table provided
Be extra careful if not same number of libraries in both tables, always manually check the matching names in 'matching_lib_names.tsv' table output
100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 16727.03it/s]
      name_Library new_lib_name
16024        syn_N        syn_N
10163        sf_N3        sf_N3
861          sf_N1        sf_N1
20745        syn_N        syn_N
10010        sf_N3        sf_N3
14134        syn_N        syn_N
9494         sf_N3        sf_N3
8268         sf_N2        sf_N2
17194        syn_N        syn_N
11192        sf_N3        sf_N3
4005         sf_N1        sf_N1
6878         sf_N2        sf_N2
8128         sf_N2        sf_N2
13253        sf_N3        sf_N3
20487        syn_N        syn_N
6690         sf_N2        sf_N2
11699        sf_N3        sf_N3
15778        syn_N        syn_N
5245         sf_N1        sf_N1
15337        syn_N        syn_N
0 libraries dont have any barcodes !!
the following libraries don't have any barcodes: set()

lib_names = pd.read_csv(lib_names_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(lib_names)

barcode_file = pd.read_csv(barcode_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

cell_type = barcode_file['cell_type'].drop_duplicates().sort_values()
cell_df = pd.DataFrame(cell_type)
cell_df = cell_df.reindex(columns=[*cell_df.columns.tolist(), 'notes_cell', 'cellTypeId_', 'cellTypeName_', 'cellTypeAnnotationStatus_', 'cell_type_new','comment_cell'], fill_value="")
cell_df.to_csv(info_celltype_path, sep="\t", index=False)
if not os.path.exists(info_celltype_complete_path): 
    cell_df.to_csv(info_celltype_complete_path, sep="\t", index=False)

# options for cellTypeAnnotationStatus: perfect match, missing child term, other

cell_df_complete = pd.read_csv(info_celltype_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(cell_df_complete)

barcode_file = barcode_file.merge(cell_df_complete, left_on='cell_type', right_on='cell_type')

barcode_file['cellTypeId'] = barcode_file['cellTypeId_'].values
barcode_file['cellTypeName'] = barcode_file['cellTypeName_'].values
barcode_file['cellTypeAnnotationStatus'] = barcode_file['cellTypeAnnotationStatus_'].values

# optional - update cell type name and add comments
#barcode_file['cell_type'] = barcode_file['cell_type_new'].values
barcode_file['comments'] = barcode_file[['comment_cell']]

barcode_file_complete = barcode_file[barcode_cols]
display_df(barcode_file_complete)
barcode_file_complete.to_csv(barcode_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

experiment = pd.read_csv(experiment_path_from_script, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
experiment.head()

library_to_add = pd.read_csv(library_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# this will give you the number of rows in the complete library file 
# this should be the number of annotated libraries
ann_lib = len(library_to_add.index)
len(library_to_add.index)

4

# partial or total
experiment.loc[:,'experimentStatus'] = 'partial'
# scRNA-seq, Sn-scRNA-seq for both we put: 'Sn-scRNA-seq, scRNA-seq'
experiment.loc[:,'RNAseqTags'] = 'scRNA-seq'


# see above cell, also can add as free text
experiment.loc[:,'numberOfAnnotatedLibraries'] = ann_lib

# these variables should already exist from above but if not can just add as free text
experiment.loc[:,'protocol'] = my_protocol
experiment.loc[:,'protocolType'] = my_protocolType

experiment.head()

experiment.loc[:,'GSE'] = 'GSE254840'
#experiment.loc[:,'Bioproject'] = '' 
experiment.loc[:,'PMID'] = '39507442'
experiment.loc[:,'reference_url'] = 'https://atm.amegroups.org/article/view/129887/html'
experiment.loc[:,'DOI'] = '10.21037/atm-24-40'
#experiment.loc[:,'xrefs'] = ''

experiment.head()

experiment.loc[:,'comment'] = 'partial b/c removed samples with osteoarthritis'

experiment.head()

experiment.to_csv(experiment_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

library_to_add = pd.read_csv(library_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
experiment_to_add = pd.read_csv(experiment_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# pull from git and pull in library/experiment file
! git pull
git_library = pd.read_csv(git_library_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
git_experiment = pd.read_csv(git_experiment_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# library file
if set(library_to_add.columns) == set(git_library.columns):
    print('The columns in the library file match')
else:
    print('The columns in the library file DO NOT MATCH')

# experiment file
if set(experiment_to_add.columns) == set(git_experiment.columns):
    print('The columns in the experiment file match')
else:
    print('The columns in the experiment file DO NOT MATCH')


# maybe to make this something more like "COLUMNS GOOD - LIBRARY" and "COLUMNS BAD - EXPERIMENT"

Already up to date.
The columns in the library file match
The columns in the experiment file match

library_git_plus_new = pd.concat([git_library, library_to_add], ignore_index = True, sort = False)
old_length = git_library.shape[0]
start = old_length - 2
end = old_length + 5
view_lib = library_git_plus_new.iloc[start:end]
view_lib

experiment_git_plus_new = pd.concat([git_experiment, experiment_to_add], ignore_index = True, sort = False)
experiment_git_plus_new.tail(n=3)

# 10X only
barcode_file_for_git = pd.read_csv(barcode_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
barcode_file_for_git.head(10)

! git pull

Already up to date.

library_git_plus_new.to_csv(git_library_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_library_path)
experiment_git_plus_new.to_csv(git_experiment_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_experiment_path)

# 10X only
barcode_file_for_git.to_csv(git_barcode_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_barcode_path)

! git status

On branch develop
Your branch is up to date with 'origin/develop'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   ../1_scRNA_template.ipynb
	modified:   ../../../scRNA_Seq/scRNASeqExperiment.tsv
	modified:   ../../../scRNA_Seq/scRNASeqLibrary_merged.tsv

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	./
	../next_ERP136630/
	../../../scRNA_Seq/scRNASeq_barcode_SRP487479.tsv

no changes added to commit (use "git add" and/or "git commit -a")

# 10X (barcode file)
! git add $git_experiment_path $git_library_path $git_barcode_path

! git status

On branch develop
Your branch is up to date with 'origin/develop'.

Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	modified:   ../../../scRNA_Seq/scRNASeqExperiment.tsv
	modified:   ../../../scRNA_Seq/scRNASeqLibrary_merged.tsv
	new file:   ../../../scRNA_Seq/scRNASeq_barcode_SRP487479.tsv

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   ../1_scRNA_template.ipynb

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	./
	../next_ERP136630/

! git commit -m $commit_message_exp

[develop 18e3dac] adding annotated scRNA experiment SRP487479
 3 files changed, 21040 insertions(+)
 create mode 100644 scRNA_Seq/scRNASeq_barcode_SRP487479.tsv

! git push

Enumerating objects: 10, done.
Counting objects: 100% (10/10), done.
Delta compression using up to 12 threads
Compressing objects: 100% (6/6), done.
Writing objects: 100% (6/6), 162.76 KiB | 5.81 MiB/s, done.
Total 6 (delta 4), reused 0 (delta 0), pack-reused 0
remote: 
remote: ========================================================================
remote: 
remote:      Hello all, Gitlab will be unavailable 2025-02-05 from 14:00 to
remote:       16:00 due to security maintenance works. Please refrain from
remote:      logging in during this time. Thank you for your understanding
remote: 
remote: ========================================================================
remote: 
remote: 
remote: To create a merge request for develop, visit:
remote:   https://gitlab.sib.swiss/Bgee/expression-annotations/-/merge_requests/new?merge_request%5Bsource_branch%5D=develop
remote: 
To https://gitlab.sib.swiss/Bgee/expression-annotations.git
   ab8b79f..18e3dac  develop -> develop

! git status

! git add $path_to_output

! git commit -m $commit_message_py

! git push

	cell_type	notes_cell	cellTypeId_	cellTypeName_	cellTypeAnnotationStatus_	comment_cell
0	Activated_CD4		CL:0000896	activated CD4-positive, alpha-beta T cell	perfect match
1	B_cell	ENSECAG00000039599, MS4A1, CD79A, TCF4, CD19, PRAG1	CL:0000236	B cell	perfect match
2	CCL2_Macrophage	CCL2 positive macrophages	CL:0000235	macrophage	missing child term
3	CD5L_Macrophage	activated (CD5L positive) macrophages - annotated as elicited macrophage because it's not clear if this is M1 or M2 macrophage, and elicited macrophage is the parent term and suggests that the macrophage is activated	CL:0000861	elicited macrophage	other
4	CX3CR1_T	CD8 T cells with cell markers: CX3CR1, ENSECAG00000031322, ENSECAG00000022193, SCML4, ZEB2	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
5	Cycling_cells	heterogenous population of cells that are actively going through the cell cycle, DEGs: PAFAH2, TOP2A, ENSECAG00000036105, H2AZ1, TUBA1A, NUSAP1, CENPF, H1-3	CL:0000255	eukaryotic cell	other	heterogenous population of cells that are actively going through the cell cycle, DEGs: PAFAH2, TOP2A, ENSECAG00000036105, H2AZ1, TUBA1A, NUSAP1, CENPF, H1-3
6	DAPL1_CD8	DAPL1 positive CD8 T cells represent a poorly demarcated cell type that did not fit into any classical T cell subset and were annotated based on expression of DAPL1, a gene that has been associated with CD8 T cell exhaustion	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
7	Effector_CD8	effector CD8 T cells	CL:0001050	effector CD8-positive, alpha-beta T cell	perfect match
8	Endothelial	VWF, ESAM1	CL:0000115	endothelial cell	perfect match
9	Fibroblast	FBLN1, COL1A1, SFRP4	CL:0000057	fibroblast	perfect match
10	GNLY_T	gd T cells: overexpression of GNLY	CL:0000798	gamma-delta T cell	missing child term
11	GPNMB_Macrophage	GPNMB positive macrophages	CL:0000235	macrophage	missing child term
12	GZMA_gd_T	gd T cells: GZMA, TRDC, BLK, and ENSECAG00000038560 expression, lacked IL23R and SCART1 expression	CL:0000798	gamma-delta T cell	missing child term
13	IFN-T	CD8 T cells enriched in interferon gene signatures	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
14	IL23R_gd_T1	gd T cells: expression of IL23R and SCART1	CL:0000798	gamma-delta T cell	missing child term
15	IL23R_gd_T2	gd T cells: expression of IL23R and SCART1	CL:0000798	gamma-delta T cell	missing child term
16	KLRD1_T	gd T cells: overexpression of KLRD1	CL:0000798	gamma-delta T cell	missing child term
17	Macrophage/DC	myeloid cells (primarily macrophages and DCs)	CL:0000763	myeloid cell	other
18	Mo-Mac	monocytes and intermediate cells with transcriptional programs suggestive of transitioning from monocytes to macrophages - annotating with parent term of monocyte & macrophage	CL:0000113	mononuclear phagocyte	other
19	Monocyte	S100A12, ENSECAG00000010117, ECATH-3, VCAN, ENSECAG00000010598, ENSECAG00000010615	CL:0000576	monocyte	perfect match
20	Myofibroblast	ACTA2, NOTCH, MCAM	CL:0000186	myofibroblast cell	perfect match
21	Neutrophil	DRAlow, CSF3R+, SELL+	CL:0000775	neutrophil	perfect match
22	SELL_CD4	naive CD4 T cells	CL:0000895	naive thymus-derived CD4-positive, alpha-beta T cell	perfect match
23	Synoviocyte_c0		CL:0000214	synovial cell	perfect match
24	Synoviocyte_c1		CL:0000214	synovial cell	perfect match
25	Synoviocyte_c2		CL:0000214	synovial cell	perfect match
26	Synoviocyte_c4		CL:0000214	synovial cell	perfect match
27	Synoviocyte_c5		CL:0000214	synovial cell	perfect match
28	Synoviocyte_c6		CL:0000214	synovial cell	perfect match
29	T cell	t cells found in synovial tissues	CL:0000084	T cell	perfect match
30	TPPP3_Macrophage	TPPP3 positive macrophages	CL:0000235	macrophage	missing child term
31	Treg	this is a subset of CD4 T cells	CL:0000792	CD4-positive, CD25-positive, alpha-beta regulatory T cell	perfect match
32	cDC1	conventional type 1 DCs: FLT3+/DNASE1L3+/BATF3+	CL:0002394	CD141-positive myeloid dendritic cell	perfect match
33	cDC2	conventional type 2 DCs: FLT3+/CD1C+	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match
34	cycling_DC	cycling DCs: FLT3+/TOP2A+	CL:4033070	cycling dendritic cell	perfect match
35	migDC	migratory DCs: FLT3+/LAMP3+/CCR7+	CL:0000451	dendritic cell	missing child term
36	pDC	plasmacytoid DCs: FLT3+/TCF4+/MS4A1+	CL:0000784	plasmacytoid dendritic cell	perfect match

	infoStage	notes_stage	stageId_	stageName_	stageAnnotationStatus_
0	13 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match
1	16 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match
2	5 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match

	infoStage	notes_stage	stageId_	stageName_	stageAnnotationStatus_
0	13 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match
1	16 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match
2	5 years	adulthood stage: starts at sexual maturity (2.6 years post-birth) and ends at senior age (20 years post-birth)	EcabDv:0000005	adulthood stage	perfect match

	#libraryId	experimentId	platform	SRSId	anatId	anatName	stageId	stageName	url_GSM	infoOrgan	infoStage	anatAnnotationStatus	stageAnnotationStatus	sex	strain	speciesId	protocol	protocolType	lib_name	sampleName	condition	lastModificationDate
0	SRX23498643	SRP487479	Illumina NovaSeq 6000	SRS20350559	UBERON:0002018	synovial membrane of synovial joint	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058731	Synovial tissue	13 years	perfect match	perfect match	M	Quarter Horse	9796	10X Genomics V3	3'end	syn_N	SAMN39727231	castrated	30/01/2025
1	SRX23498639	SRP487479	Illumina NovaSeq 6000	SRS20350555	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058727	Synovial fluid	16 years	perfect match	perfect match	M	Quarter Horse	9796	10X Genomics V3	3'end	sf_N3	SAMN39727235	castrated	30/01/2025
2	SRX23498638	SRP487479	Illumina NovaSeq 6000	SRS20350553	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058726	Synovial fluid	13 years	perfect match	perfect match	M	Quarter Horse	9796	10X Genomics V3	3'end	sf_N2	SAMN39727236	castrated	30/01/2025
3	SRX23498637	SRP487479	Illumina NovaSeq 6000	SRS20350554	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058725	Synovial fluid	5 years	perfect match	perfect match	M	Quarter Horse	9796	10X Genomics V3	3'end	sf_N1	SAMN39727237	castrated	30/01/2025

	cell_type	notes_cell	cellTypeId_	cellTypeName_	cellTypeAnnotationStatus_	comment_cell
0	Activated_CD4		CL:0000896	activated CD4-positive, alpha-beta T cell	perfect match
1	B_cell	ENSECAG00000039599, MS4A1, CD79A, TCF4, CD19, PRAG1	CL:0000236	B cell	perfect match
2	CCL2_Macrophage	CCL2 positive macrophages	CL:0000235	macrophage	missing child term
3	CD5L_Macrophage	activated (CD5L positive) macrophages - annotated as elicited macrophage because it's not clear if this is M1 or M2 macrophage, and elicited macrophage is the parent term and suggests that the macrophage is activated	CL:0000861	elicited macrophage	other
4	CX3CR1_T	CD8 T cells with cell markers: CX3CR1, ENSECAG00000031322, ENSECAG00000022193, SCML4, ZEB2	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
5	Cycling_cells	heterogenous population of cells that are actively going through the cell cycle, DEGs: PAFAH2, TOP2A, ENSECAG00000036105, H2AZ1, TUBA1A, NUSAP1, CENPF, H1-3	CL:0000255	eukaryotic cell	other	heterogenous population of cells that are actively going through the cell cycle, DEGs: PAFAH2, TOP2A, ENSECAG00000036105, H2AZ1, TUBA1A, NUSAP1, CENPF, H1-3
6	DAPL1_CD8	DAPL1 positive CD8 T cells represent a poorly demarcated cell type that did not fit into any classical T cell subset and were annotated based on expression of DAPL1, a gene that has been associated with CD8 T cell exhaustion	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
7	Effector_CD8	effector CD8 T cells	CL:0001050	effector CD8-positive, alpha-beta T cell	perfect match
8	Endothelial	VWF, ESAM1	CL:0000115	endothelial cell	perfect match
9	Fibroblast	FBLN1, COL1A1, SFRP4	CL:0000057	fibroblast	perfect match
10	GNLY_T	gd T cells: overexpression of GNLY	CL:0000798	gamma-delta T cell	missing child term
11	GPNMB_Macrophage	GPNMB positive macrophages	CL:0000235	macrophage	missing child term
12	GZMA_gd_T	gd T cells: GZMA, TRDC, BLK, and ENSECAG00000038560 expression, lacked IL23R and SCART1 expression	CL:0000798	gamma-delta T cell	missing child term
13	IFN-T	CD8 T cells enriched in interferon gene signatures	CL:0000625	CD8-positive, alpha-beta T cell	missing child term
14	IL23R_gd_T1	gd T cells: expression of IL23R and SCART1	CL:0000798	gamma-delta T cell	missing child term
15	IL23R_gd_T2	gd T cells: expression of IL23R and SCART1	CL:0000798	gamma-delta T cell	missing child term
16	KLRD1_T	gd T cells: overexpression of KLRD1	CL:0000798	gamma-delta T cell	missing child term
17	Macrophage/DC	myeloid cells (primarily macrophages and DCs)	CL:0000763	myeloid cell	other
18	Mo-Mac	monocytes and intermediate cells with transcriptional programs suggestive of transitioning from monocytes to macrophages - annotating with parent term of monocyte & macrophage	CL:0000113	mononuclear phagocyte	other
19	Monocyte	S100A12, ENSECAG00000010117, ECATH-3, VCAN, ENSECAG00000010598, ENSECAG00000010615	CL:0000576	monocyte	perfect match
20	Myofibroblast	ACTA2, NOTCH, MCAM	CL:0000186	myofibroblast cell	perfect match
21	Neutrophil	DRAlow, CSF3R+, SELL+	CL:0000775	neutrophil	perfect match
22	SELL_CD4	naive CD4 T cells	CL:0000895	naive thymus-derived CD4-positive, alpha-beta T cell	perfect match
23	Synoviocyte_c0		CL:0000214	synovial cell	perfect match
24	Synoviocyte_c1		CL:0000214	synovial cell	perfect match
25	Synoviocyte_c2		CL:0000214	synovial cell	perfect match
26	Synoviocyte_c4		CL:0000214	synovial cell	perfect match
27	Synoviocyte_c5		CL:0000214	synovial cell	perfect match
28	Synoviocyte_c6		CL:0000214	synovial cell	perfect match
29	T cell	t cells found in synovial tissues	CL:0000084	T cell	perfect match
30	TPPP3_Macrophage	TPPP3 positive macrophages	CL:0000235	macrophage	missing child term
31	Treg	this is a subset of CD4 T cells	CL:0000792	CD4-positive, CD25-positive, alpha-beta regulatory T cell	perfect match
32	cDC1	conventional type 1 DCs: FLT3+/DNASE1L3+/BATF3+	CL:0002394	CD141-positive myeloid dendritic cell	perfect match
33	cDC2	conventional type 2 DCs: FLT3+/CD1C+	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match
34	cycling_DC	cycling DCs: FLT3+/TOP2A+	CL:4033070	cycling dendritic cell	perfect match
35	migDC	migratory DCs: FLT3+/LAMP3+/CCR7+	CL:0000451	dendritic cell	missing child term
36	pDC	plasmacytoid DCs: FLT3+/TCF4+/MS4A1+	CL:0000784	plasmacytoid dendritic cell	perfect match

SRP487479 / GSE254840¶

annotation summary¶

set variables, import packages, define functions¶

script¶

library annnotations¶

anatomical entity¶

re-read library file¶

stage¶

sex, strain, genotype, speciesId¶

protocol & RNAseqTags¶

condition¶

annotator id, last modification date¶

comments¶

save complete file with correct columns¶

script - barcode (10X only)¶

check library names match¶

cell type annotation - 10X version¶

experiment annotations¶

experiment and protocol details¶

paper and xrefs¶

comments¶

save complete file¶

QA time¶

to add things here¶

check columns match¶

view files¶

add annotations to git¶

add annotation folder and script to git¶

	#libraryId	experimentId	platform	SRSId	anatId	anatName	stageId	stageName	url_GSM	infoOrgan	infoStage	anatAnnotationStatus	stageAnnotationStatus	sex	strain	speciesId	RNAseqTags	protocol	protocolType	lib_name	sampleName	condition	annotatorId	lastModificationDate
59071	ERX6700458	ERP132587	NextSeq 550	ERS8071686	UBERON:0000473	testis	PpanDv:0000025	15-year-old stage		testis	15 years	perfect match	perfect match	M		9597	Sn-scRNA-seq	10X Genomics V2	3'end	SN224	SAMEA10418842		SAC	2025-01-28
59072	ERX6700457	ERP132587	NextSeq 550	ERS8071685	UBERON:0000473	testis	PpanDv:0000040	aged stage		testis	36 years	perfect match	missing child term	M		9597	Sn-scRNA-seq	10X Genomics V2	3'end	SN219	SAMEA10418841		SAC	2025-01-28
59073	SRX23498643	SRP487479	Illumina NovaSeq 6000	SRS20350559	UBERON:0002018	synovial membrane of synovial joint	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Synovial tissue	13 years	perfect match	perfect match	M	Quarter Horse	9796	scRNA-seq	10X Genomics V3.1	3'end	syn_N	SAMN39727231	castrated	SAC	2024-01-30
59074	SRX23498639	SRP487479	Illumina NovaSeq 6000	SRS20350555	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Synovial fluid	16 years	perfect match	perfect match	M	Quarter Horse	9796	scRNA-seq	10X Genomics V3.1	3'end	sf_N3	SAMN39727235	castrated	SAC	2024-01-30
59075	SRX23498638	SRP487479	Illumina NovaSeq 6000	SRS20350553	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Synovial fluid	13 years	perfect match	perfect match	M	Quarter Horse	9796	scRNA-seq	10X Genomics V3.1	3'end	sf_N2	SAMN39727236	castrated	SAC	2024-01-30
59076	SRX23498637	SRP487479	Illumina NovaSeq 6000	SRS20350554	UBERON:0001090	synovial fluid	EcabDv:0000005	adulthood stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Synovial fluid	5 years	perfect match	perfect match	M	Quarter Horse	9796	scRNA-seq	10X Genomics V3.1	3'end	sf_N1	SAMN39727237	castrated	SAC	2024-01-30

	#experimentId	experimentName	experimentDescription	experimentSource	experimentStatus	RNAseqTags	numberOfAnnotatedLibraries	protocol	protocolType	GSE	Bioproject	PMID	reference_url	DOI	xrefs	comment
107	SRP485481	Single-Cell RNA Sequencing Investigation of th...	We used single-cell RNA sequencing to characte...	SRA	partial	scRNA-seq	3	10X Genomics V3	3'end	GSE254005	PRJNA1068098	38933260	https://pmc.ncbi.nlm.nih.gov/articles/PMC11199...	10.3389/fimmu.2024.1397590		only normal libraries have been reported in ou...
108	ERP132587	snRNA-seq of bonobo (Pan paniscus) adult testis	Single-nucleus RNA sequencing (snRNA-seq) was ...	ENA	total	Sn-scRNA-seq	2	10X Genomics V2	3'end		PRJEB48245	36544022	https://www.nature.com/articles/s41586-022-055...	10.1038/s41586-022-05547-7	E-MTAB-11067[ArrayExpress]	bonobo single cell experiment from PMID 36544022
109	SRP487479	Transcriptomic response in osteoarthritis dete...	We used single-cell RNA sequencing to characte...	SRA	partial	scRNA-seq	4	10X Genomics V3.1	3'end	GSE254840	PRJNA1072060	39507442	https://atm.amegroups.org/article/view/129887/...	10.21037/atm-24-40		partial b/c removed samples with osteoarthritis

	barcode	library	experiment	tissue	cell_type	cellTypeId	cellTypeName	cellTypeAnnotationStatus	name_Library
0	AAACCCACAAGTGGTG	SRX23498637	SRP487479	Synovial fluid	CD5L_Macrophage	CL:0000861	elicited macrophage	other	sf_N1
1	AAACCCAGTTCTCTCG	SRX23498637	SRP487479	Synovial fluid	cDC2	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match	sf_N1
2	AAACCCATCGAGAAAT	SRX23498637	SRP487479	Synovial fluid	CD5L_Macrophage	CL:0000861	elicited macrophage	other	sf_N1
3	AAACGAAAGGAGGGTG	SRX23498637	SRP487479	Synovial fluid	Neutrophil	CL:0000775	neutrophil	perfect match	sf_N1
4	AAACGAACACTTTATC	SRX23498637	SRP487479	Synovial fluid	GPNMB_Macrophage	CL:0000235	macrophage	missing child term	sf_N1
5	AAACGAACATATCGGT	SRX23498637	SRP487479	Synovial fluid	cDC2	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match	sf_N1
6	AAACGCTAGGACGCTA	SRX23498637	SRP487479	Synovial fluid	cDC2	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match	sf_N1
7	AAACGCTAGTGGATAT	SRX23498637	SRP487479	Synovial fluid	cDC2	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match	sf_N1
8	AAACGCTCAACATACC	SRX23498637	SRP487479	Synovial fluid	cDC2	CL:0002399	CD1c-positive myeloid dendritic cell	perfect match	sf_N1
9	AAACGCTGTAGGGTAC	SRX23498637	SRP487479	Synovial fluid	cDC1	CL:0002394	CD141-positive myeloid dendritic cell	perfect match	sf_N1

	#libraryId	experimentId	platform	SRSId	url_GSM	infoOrgan	speciesId	protocol	protocolType	lib_name	sampleName	lastModificationDate	library_contruction_protocol	source_name
0	SRX23498643	SRP487479	Illumina NovaSeq 6000	SRS20350559	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058731	Synovial tissue	9796	10X Genomics V3	3'end	syn_N	SAMN39727231	30/01/2025	Synovial fluid (5 to 10 mL) was sampled from the affected middle carpal joint (osteoarthritic sample) and tibiotarsal joint (normal sample). Synoviocenteses to obtain synovial fluid were performed implementing aseptic technique using sterile gloves, needles (20 gauge, 1.5 inches), and syringes (23). Synovial fluid samples were washed with PBS, filtered through a 40 µM cell strainer, then red blood cells were removed using Ammonium-Chloride-Potassium (ACK) lysis buffer. Red blood cell depleted suspensions were subsequently resuspended in PBS with 0.04% molecular grade BSA (Sigma-Aldrich; St. Louis, MO). Synovium was obtained immediately following euthanasia. Briefly, the horse was euthanized with pentobarbital (1 mL / 5 kg body weight) administered via indwelling catheter, then the middle carpal and tibiotarsal joints were dissected aseptically and synovium was sampled from four quadrants of each joint (dorsomedial, dorsolateral, palmar- or plantaromedial, palmar- or plantarolateral) and pooled for analysis. Synovial tissue samples were then disaggregated into single cell suspensions. Briefly, synovial tissue fragments were separated mechanically and enzymatically into digestion buffer (Liberase TL (Sigma-Aldrich) 100 µg/mL and DNAase I (New England Biolabs) 100 µg/mL in RPMI) in a 37°C water bath for 30 minutes. Digested suspensions were washed with PBS then resuspended in PBS with 0.04% non-acetylated BSA (Sigma-Aldrich). To remove small debris and platelets, a final wash at 100 rcf for 15 minutes was completed. The samples were then prepared to be run on a Chomium iX instument by resusspending in 0.04% molecular grade BSA. The instrument was loaded to target 5,000 cells per sample. Library construction was performed according to the manufacturer's instructions (Chromium Next GEM Single Cell 3ʹ Kit v3.1 protocol, 10x Genomics). Briefly, a Chromium iX instrument was used to pass cells and oligonucleotide coated gel beads at a limiting dilution to obtain a coupling of cells and beads in a 1:1 ratio. The cell-bead pair was then passed from an aqueous phase to an oil phase to form an emulsion. Within the emulsions, cells were lysed, and the oligonucleotide sequences on the gel beads pull down message RNA and other RNA molecules that have long adenosine stretches with an oligo-d(T) pull down. After hybridization, reverse transcription is completed to generate a cDNA library with each transcript now associated with a unique cell barcode and an Illumina R1 primer sequence. Once cells were barcoded and unique molecular identifiers (UMIs) added, a standard Illumina library preparation kit was completed with SPRIselect used for fragment size selection. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.	Synovial tissue
1	SRX23498639	SRP487479	Illumina NovaSeq 6000	SRS20350555	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058727	Synovial fluid	9796	10X Genomics V3	3'end	sf_N3	SAMN39727235	30/01/2025	Synovial fluid (5 to 10 mL) was sampled from the affected middle carpal joint (osteoarthritic sample) and tibiotarsal joint (normal sample). Synoviocenteses to obtain synovial fluid were performed implementing aseptic technique using sterile gloves, needles (20 gauge, 1.5 inches), and syringes (23). Synovial fluid samples were washed with PBS, filtered through a 40 µM cell strainer, then red blood cells were removed using Ammonium-Chloride-Potassium (ACK) lysis buffer. Red blood cell depleted suspensions were subsequently resuspended in PBS with 0.04% molecular grade BSA (Sigma-Aldrich; St. Louis, MO). Synovium was obtained immediately following euthanasia. Briefly, the horse was euthanized with pentobarbital (1 mL / 5 kg body weight) administered via indwelling catheter, then the middle carpal and tibiotarsal joints were dissected aseptically and synovium was sampled from four quadrants of each joint (dorsomedial, dorsolateral, palmar- or plantaromedial, palmar- or plantarolateral) and pooled for analysis. Synovial tissue samples were then disaggregated into single cell suspensions. Briefly, synovial tissue fragments were separated mechanically and enzymatically into digestion buffer (Liberase TL (Sigma-Aldrich) 100 µg/mL and DNAase I (New England Biolabs) 100 µg/mL in RPMI) in a 37°C water bath for 30 minutes. Digested suspensions were washed with PBS then resuspended in PBS with 0.04% non-acetylated BSA (Sigma-Aldrich). To remove small debris and platelets, a final wash at 100 rcf for 15 minutes was completed. The samples were then prepared to be run on a Chomium iX instument by resusspending in 0.04% molecular grade BSA. The instrument was loaded to target 5,000 cells per sample. Library construction was performed according to the manufacturer's instructions (Chromium Next GEM Single Cell 3ʹ Kit v3.1 protocol, 10x Genomics). Briefly, a Chromium iX instrument was used to pass cells and oligonucleotide coated gel beads at a limiting dilution to obtain a coupling of cells and beads in a 1:1 ratio. The cell-bead pair was then passed from an aqueous phase to an oil phase to form an emulsion. Within the emulsions, cells were lysed, and the oligonucleotide sequences on the gel beads pull down message RNA and other RNA molecules that have long adenosine stretches with an oligo-d(T) pull down. After hybridization, reverse transcription is completed to generate a cDNA library with each transcript now associated with a unique cell barcode and an Illumina R1 primer sequence. Once cells were barcoded and unique molecular identifiers (UMIs) added, a standard Illumina library preparation kit was completed with SPRIselect used for fragment size selection. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.	Synovial fluid
2	SRX23498638	SRP487479	Illumina NovaSeq 6000	SRS20350553	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058726	Synovial fluid	9796	10X Genomics V3	3'end	sf_N2	SAMN39727236	30/01/2025	Synovial fluid (5 to 10 mL) was sampled from the affected middle carpal joint (osteoarthritic sample) and tibiotarsal joint (normal sample). Synoviocenteses to obtain synovial fluid were performed implementing aseptic technique using sterile gloves, needles (20 gauge, 1.5 inches), and syringes (23). Synovial fluid samples were washed with PBS, filtered through a 40 µM cell strainer, then red blood cells were removed using Ammonium-Chloride-Potassium (ACK) lysis buffer. Red blood cell depleted suspensions were subsequently resuspended in PBS with 0.04% molecular grade BSA (Sigma-Aldrich; St. Louis, MO). Synovium was obtained immediately following euthanasia. Briefly, the horse was euthanized with pentobarbital (1 mL / 5 kg body weight) administered via indwelling catheter, then the middle carpal and tibiotarsal joints were dissected aseptically and synovium was sampled from four quadrants of each joint (dorsomedial, dorsolateral, palmar- or plantaromedial, palmar- or plantarolateral) and pooled for analysis. Synovial tissue samples were then disaggregated into single cell suspensions. Briefly, synovial tissue fragments were separated mechanically and enzymatically into digestion buffer (Liberase TL (Sigma-Aldrich) 100 µg/mL and DNAase I (New England Biolabs) 100 µg/mL in RPMI) in a 37°C water bath for 30 minutes. Digested suspensions were washed with PBS then resuspended in PBS with 0.04% non-acetylated BSA (Sigma-Aldrich). To remove small debris and platelets, a final wash at 100 rcf for 15 minutes was completed. The samples were then prepared to be run on a Chomium iX instument by resusspending in 0.04% molecular grade BSA. The instrument was loaded to target 5,000 cells per sample. Library construction was performed according to the manufacturer's instructions (Chromium Next GEM Single Cell 3ʹ Kit v3.1 protocol, 10x Genomics). Briefly, a Chromium iX instrument was used to pass cells and oligonucleotide coated gel beads at a limiting dilution to obtain a coupling of cells and beads in a 1:1 ratio. The cell-bead pair was then passed from an aqueous phase to an oil phase to form an emulsion. Within the emulsions, cells were lysed, and the oligonucleotide sequences on the gel beads pull down message RNA and other RNA molecules that have long adenosine stretches with an oligo-d(T) pull down. After hybridization, reverse transcription is completed to generate a cDNA library with each transcript now associated with a unique cell barcode and an Illumina R1 primer sequence. Once cells were barcoded and unique molecular identifiers (UMIs) added, a standard Illumina library preparation kit was completed with SPRIselect used for fragment size selection. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.	Synovial fluid
3	SRX23498637	SRP487479	Illumina NovaSeq 6000	SRS20350554	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8058725	Synovial fluid	9796	10X Genomics V3	3'end	sf_N1	SAMN39727237	30/01/2025	Synovial fluid (5 to 10 mL) was sampled from the affected middle carpal joint (osteoarthritic sample) and tibiotarsal joint (normal sample). Synoviocenteses to obtain synovial fluid were performed implementing aseptic technique using sterile gloves, needles (20 gauge, 1.5 inches), and syringes (23). Synovial fluid samples were washed with PBS, filtered through a 40 µM cell strainer, then red blood cells were removed using Ammonium-Chloride-Potassium (ACK) lysis buffer. Red blood cell depleted suspensions were subsequently resuspended in PBS with 0.04% molecular grade BSA (Sigma-Aldrich; St. Louis, MO). Synovium was obtained immediately following euthanasia. Briefly, the horse was euthanized with pentobarbital (1 mL / 5 kg body weight) administered via indwelling catheter, then the middle carpal and tibiotarsal joints were dissected aseptically and synovium was sampled from four quadrants of each joint (dorsomedial, dorsolateral, palmar- or plantaromedial, palmar- or plantarolateral) and pooled for analysis. Synovial tissue samples were then disaggregated into single cell suspensions. Briefly, synovial tissue fragments were separated mechanically and enzymatically into digestion buffer (Liberase TL (Sigma-Aldrich) 100 µg/mL and DNAase I (New England Biolabs) 100 µg/mL in RPMI) in a 37°C water bath for 30 minutes. Digested suspensions were washed with PBS then resuspended in PBS with 0.04% non-acetylated BSA (Sigma-Aldrich). To remove small debris and platelets, a final wash at 100 rcf for 15 minutes was completed. The samples were then prepared to be run on a Chomium iX instument by resusspending in 0.04% molecular grade BSA. The instrument was loaded to target 5,000 cells per sample. Library construction was performed according to the manufacturer's instructions (Chromium Next GEM Single Cell 3ʹ Kit v3.1 protocol, 10x Genomics). Briefly, a Chromium iX instrument was used to pass cells and oligonucleotide coated gel beads at a limiting dilution to obtain a coupling of cells and beads in a 1:1 ratio. The cell-bead pair was then passed from an aqueous phase to an oil phase to form an emulsion. Within the emulsions, cells were lysed, and the oligonucleotide sequences on the gel beads pull down message RNA and other RNA molecules that have long adenosine stretches with an oligo-d(T) pull down. After hybridization, reverse transcription is completed to generate a cDNA library with each transcript now associated with a unique cell barcode and an Illumina R1 primer sequence. Once cells were barcoded and unique molecular identifiers (UMIs) added, a standard Illumina library preparation kit was completed with SPRIselect used for fragment size selection. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.	Synovial fluid